Suspending data replication

ABSTRACT

In one aspect, a method includes monitoring a replication process, predicting performance based on the monitoring, receiving a suspend notification and determining whether to suspend the replication process or wait to suspend the replication process based on the performance predicted. In another aspect, an apparatus includes electronic hardware circuitry configured to monitor a replication process, predict performance based on the monitoring, receive a suspend notification and determine whether to suspend the replication process or wait to suspend the replication process based on the performance predicted. In a further aspect, an apparatus includes electronic hardware circuitry configured to: monitor a replication process, predict performance based on the monitoring, receive a suspend notification and determine whether to suspend the replication process or wait to suspend the replication process based on the performance predicted.

BACKGROUND

Storage systems in general, and block based storage systemsspecifically, are a key element in modern data centers and computinginfrastructure. These systems are designed to store and retrieve largeamounts of data, by providing data block address and data blockcontent—for storing a block of data—and by providing a data blockaddress for retrieval of the data block content that is stored at thespecified address.

Storage solutions are typically partitioned into categories based on ause case and application within a computing infrastructure, and a keydistinction exists between primary storage solutions and archivingstorage solutions. Primary storage is typically used as the main storagepool for computing applications during application run-time. As such,the performance of primary storage systems is very often a key challengeand a major potential bottleneck in overall application performance,since storage and retrieval of data consumes time and delays thecompletion of application processing. Storage systems designed forarchiving applications are much less sensitive to performanceconstraints, as they are not part of the run-time applicationprocessing.

In general computer systems grow over their lifetime and the data undermanagement tends to grow over the system lifetime. Growth can beexponential, and in both primary and archiving storage systems,exponential capacity growth typical in modern computing environmentpresents a major challenge as it results in increased cost, space, andpower consumption of the storage systems required to support everincreasing amounts of information.

Existing storage solutions, and especially primary storage solutions,rely on address-based mapping of data, as well as address-basedfunctionality of the storage system's internal algorithms. This is onlynatural since the computing applications always rely on address-basedmapping and identification of data they store and retrieve. However, acompletely different scheme in which data, internally within the storagesystem, is mapped and managed based on its content instead of itsaddress has many substantial advantages. For example, it improvesstorage capacity efficiency since any duplicate block data will onlyoccupy actual capacity of a single instance of that block. As anotherexample, it improves performance since duplicate block writes do notneed to be executed internally in the storage system. Existing storagesystems, either primary storage systems or archiving storage systems areincapable of supporting the combination of content based storage—withits numerous advantages—and ultra-high performance. This is a result ofthe fact that the implementation of content based storage scheme facesseveral challenges:

(a) intensive computational load which is not easily distributable orbreakable into smaller tasks,

(b) an inherent need to break large blocks into smaller block sizes inorder to achieve content addressing at fine granularity. This blockfragmentation dramatically degrades the performance of existing storagesolutions,

(c) inability to maintain sequential location of data blocks within thestorage systems, since mapping is not address based any more, and suchinability causes dramatic performance degradation with traditionalspinning disk systems,

(d) the algorithmic and architectural difficulty in distributing thetasks associated with content based mapping over a large number ofprocessing and storage elements while maintaining singlecontent-addressing space over the full capacity range of the storagesystem.

A number of issues arise with respect to such devices, and it isnecessary to consider such issues as performance, lifetime andresilience to failure of individual devices, overall speed of responseand the like.

Such devices may be used in highly demanding circumstances where failureto process data correctly can be extremely serious, or where largescales are involved, and where the system has to be able to cope withsudden surges in demand.

SUMMARY

In one aspect, a method includes monitoring a replication process,predicting performance based on the monitoring, receiving a suspendnotification and determining whether to suspend the replication processor wait to suspend the replication process based on the performancepredicted. In another aspect, an apparatus includes electronic hardwarecircuitry configured to monitor a replication process, predictperformance based on the monitoring, receive a suspend notification anddetermine whether to suspend the replication process or wait to suspendthe replication process based on the performance predicted. In a furtheraspect, an apparatus includes electronic hardware circuitry configuredto: monitor a replication process, predict performance based on themonitoring, receive a suspend notification and determine whether tosuspend the replication process or wait to suspend the replicationprocess based on the performance predicted.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified diagram schematically illustrating a system fordata storage, having separate control and data planes.

FIG. 2 shows an exemplary configuration of modules for the system ofFIG. 1.

FIG. 3 is a simplified diagram schematically illustrating four differentnode configurations for the system.

FIG. 4 is a simplified schematic diagram showing the nodes of FIG. 3connected to a switch.

FIG. 5 is a simplified diagram showing a compute+SAN+store node for thedevice of FIG. 1.

FIG. 6 is a simplified flow chart which illustrates the flow for a readoperation for one aligned X-page.

FIG. 7 is a simplified flow chart which illustrates the flow in theevent that a read request arrives for a range of addresses spanning morethan one X-Page but only one SL.

FIG. 8 is a simplified flow diagram illustrating the write procedure fora single aligned X page.

FIG. 9 is a simplified flow diagram illustrating the process forperforming write operations to multiple full X-Pages.

FIG. 10 is a simplified block diagram of an example of a replicationsystem.

FIG. 11 is a simplified flowchart of an example of a process to suspenddata replication.

FIG. 12 is a computer on which all or part of the process of FIG. 11 maybe implemented.

DETAILED DESCRIPTION

Asynchronous snapshot based remote replication provides data protectionagainst site disaster with minimal impact to host I/O performance.Asynchronous snapshot based remote replication generates snapshots,calculates differences (deltas) between the snapshots, periodicallytransfers the differenced to remote sites and reconstructs data content.Once a user sets up a replication relationship, the system automaticallyschedules the data transfer without user intervention. In a distributedsystem, the system further divides the work of calculating and sendingthe differences (deltas) among worker threads and clustered nodes. Attimes, there is a need to suspend the replication data transferactivity. One such example is in real or simulated disaster recoveryscenario, where a reliable way is needed to stop ongoing replicationactivity while preserving data transfer already been done. Other casesthat may trigger suspend/abort replication data transfer includes:network reconfiguration, storage array software upgrade and so forth.Since the suspend event/command could come at any state of replication,it is desirable that the system builds have the capability to handlesuspend requests in an optimum cost efficient and performant manner.Additionally, it is desirable to distinguish between cases where it ischeaper to abort replication than to wait for a cycle to complete.Described herein ate techniques that determine when to suspendreplication.

Before describing the specific techniques, FIGS. 1 to 9 describecomponents that may be used in an example replication system. However,one of ordinary skill in the art would recognize that any of thetechniques described herein may be applied to numerous other replicationsystems to suspend replication.

In a Content Addressable Storage (CAS) array, data is stored in blocks,for example of 4 KB, where each block has a unique large hash signature,for example of 20 bytes, saved on Flash memory.

The examples described herein include a networked memory system. Thenetworked memory system includes multiple memory storage units arrangedfor content addressable storage of data. The data is transferred to andfrom the storage units using separate data and control planes. Hashingis used for the content addressing, and the hashing produces evenlydistributed results over the allowed input range. The hashing definesthe physical addresses so that data storage makes even use of the systemresources.

A relatively small granularity may be used, for example with a page sizeof 4 KB, although smaller or larger block sizes may be selected at thediscretion of the skilled person. This enables the device to detach theincoming user access pattern from the internal access pattern. That isto say the incoming user access pattern may be larger than the 4 KB orother system-determined page size and may thus be converted to aplurality of write operations within the system, each one separatelyhashed and separately stored.

Content addressable data storage can be used to ensure that dataappearing twice is stored at the same location. Hence unnecessaryduplicate write operations can be identified and avoided. Such a featuremay be included in the present system as data deduplication. As well asmaking the system more efficient overall, it also increases the lifetimeof those storage units that are limited by the number of write/eraseoperations.

The separation of Control and Data may enable a substantially unlimitedlevel of scalability, since control operations can be split over anynumber of processing elements, and data operations can be split over anynumber of data storage elements. This allows scalability in bothcapacity and performance, and may thus permit an operation to beeffectively balanced between the different modules and nodes.

The separation may also help to speed the operation of the system. Thatis to say it may speed up Writes and Reads. Such may be due to:

(a) Parallel operation of certain Control and Data actions over multipleNodes/Modules

(b) Use of optimal internal communication/networking technologies perthe type of operation (Control or Data), designed to minimize thelatency (delay) and maximize the throughput of each type of operation.

Also, separation of control and data paths may allow each Control orData information unit to travel within the system between Nodes orModules in the optimal way, meaning only to where it is needed andif/when it is needed. The set of optimal where and when coordinates isnot the same for control and data units, and hence the separation ofpaths ensures the optimization of such data and control movements, in away which is not otherwise possible. The separation is important inkeeping the workloads and internal communications at the minimumnecessary, and may translate into increased optimization of performance.

De-duplication of data, meaning ensuring that the same data is notstored twice in different places, is an inherent effect of usingContent-Based mapping of data to D-Modules and within D-Modules.

Scalability is inherent to the architecture. Nothing in the architecturelimits the number of the different R, C, D, and H modules which aredescribed further herein. Hence any number of such modules can beassembled. The more modules added, the higher the performance of thesystem becomes and the larger the capacity it can handle. Hencescalability of performance and capacity is achieved.

The principles and operation of an apparatus and method according to thepresent invention may be better understood with reference to thedrawings and accompanying description.

Reference is now made to FIG. 1 which illustrates a system 10 forscalable block data storage and retrieval using content addressing. Thesystem 10 includes data storage devices 12 on which the data blocks arestored. The storage devices 12 are networked to computing modules, therebeing several kinds of modules, including control modules 14 and datamodules 16. The modules carry out content addressing for storage andretrieval, and the network defines separate paths or planes, controlpaths or a control plane which goes via the control modules 14 and datapaths or a data plane which goes via the data modules 16.

The control modules 14 may control execution of read and write commands.The data modules 16 are connected to the storage devices and, undercontrol of a respective control module, pass data to or from the storagedevices. Both the C and D modules may retain extracts of the data storedin the storage device, and the extracts may be used for the contentaddressing. Typically the extracts may be computed by cryptographichashing of the data, as will be discussed in greater detail below, andhash modules (FIG. 2) may specifically be provided for this purpose.That is to say the hash modules calculate hash values for data which isthe subject of storage commands, and the hash values calculated maylater be used for retrieval.

Routing modules 18 may terminate storage and retrieval operations anddistribute command parts of any operations to control modules that areexplicitly selected for the operation in such a way as to retainbalanced usage within the system 10.

The routing modules may use hash values, calculated from data associatedwith the operations, to select the control module for the distribution.More particularly, selection of the control module may use hash values,but typically relies on the user address and not on the content (hash).The hash value is, however, typically used for selecting the Data (D)module, and for setting the physical location for data storage within aD module.

The storage devices may be solid state random access storage devices, asopposed to spinning disk devices; however disk devices may be usedinstead or in addition.

A deduplication feature may be provided. The routing modules and/or datamodules may compare the extracts or hash values of write data with hashvalues of already stored data, and where a match is found, simply pointto the matched data and avoid rewriting.

The modules are combined into nodes 20 on the network, and the nodes areconnected over the network by a switch 22.

The use of content addressing with multiple data modules selected on thebasis of the content hashing, and a finely-grained mapping of useraddresses to Control Modules allow for a scalable distributedarchitecture.

A glossary is now given of terms used in the following description:

X-PAGE—A predetermined-size aligned chunk as the base unit for memoryand disk operations. Throughout the present description the X-Page sizeis referred to as having 4 KB, however other smaller or larger valuescan be used as well and nothing in the design is limited to a specificvalue.

LUN or LOGICAL UNIT NUMBER is a common name in the industry fordesignating a volume of data, or a group of data blocks being named withthe LUN. Each data block is referred to, by the external user of thestorage system, according to its LUN, and its address within this LUN

LOGICAL X-PAGE ADDRESS—Logical address of an X-Page. The addresscontains a LUN identifier as well as the offset of the X-Page within theLUN.

LOGICAL BLOCK—512 bytes (sector) aligned chunk, which is the SCSI baseunit for disk operations.

LOGICAL BLOCK ADDRESS—Logical address of a Logical Block. The logicalblock address contains a LUN identifier as well as the offset of thelogical block within the LUN.

SUB-LUN-Division of a LUN to smaller logical areas, to balance the loadbetween C modules. Each such small logical area is called a sub-LUN.

SUB-LUN UNIT SIZE—The fixed size of a sub-LUN. X-Page Data-Specificsequence of user data values that resides in an X-Page. Each such X-PageData is uniquely represented in the system by its hash digest.

D PRIMARY—The D module responsible for storing an X-Page's Data

D BACKUP—The D module responsible for storing a backup for an X-PageData. The backup is stored in a non-volatile way (NVRAM or UPSprotected).

Acronyms:

LXA—Logical X-Page Address.

LB—Logical Block.

LBA—Logical Block Address.

AUS—Atomic Unit Size.

SL—Sub-LUN.

SLUS—Sub-LUN Unit Size.

MBE—Management Back End.

The examples described herein to a block-level storage system, offeringbasic and advanced storage functionality. The design may be based on adistributed architecture, where computational, Storage Area Networking(SAN), and storage elements are distributed over multiple physicalNodes, with all such Nodes being inter-connected over an internalnetwork through a switch device. The distributed architecture enablesthe scaling of the system's capabilities in multiple aspects, includingoverall storage capacity, performance characteristics in bandwidth andI/O operations per second (IOPS), computational resources, internal andexternal networking bandwidth, and other. While being based on adistributed architecture, the system presents, externally, a unifiedstorage system entity with scalable capabilities.

The system's architecture and internal algorithms implementing the basicand advanced storage functions are optimized for improved utilization ofthe capabilities of random-access memory/storage media, as opposed tocontrast with mechanical-magnetic spinning disk storage media. Theoptimizations are implemented in the design itself, and may, forexample, include the ability to break incoming writes into smallerblocks and distribute the operation over different Nodes. Such anadaptation is particularly suitable for random access memory/storagemedia but is less suitable in a spinning-disk environment, as it woulddegrade performance to extremely low levels. The adaptation includes thecontent/hash based mapping of data distributes the data over different DNodes in general and within D Nodes over different SSD devices. Again,such a scheme is more suitable for random access memory/storage mediathan for a spinning-disk media because such spread of data blocks wouldresult in very poor performance in the spinning disk case. That is tosay, the described elements of the present architecture are designed towork well with random access media, and achieve benefits in performance,scalability, and functionality such as inline deduplication. Suchrandom-access memory media can be based on any or a combination of flashmemory, DRAM, phase change memory, or other memory technology, whetherpersistent or non-persistent, and is typically characterized by randomseek/access times and random read/write speeds substantially higher thanthose exhibited by spinning disk media. The system's internal data blockmapping, the algorithms implementing advanced storage functions, and thealgorithms for protecting data stored in the system are designed toprovide storage performance and advanced storage functionality atsubstantially higher performance, speed, and flexibility than thoseavailable with alternative storage systems.

Data mapping within the system is designed not only to improveperformance, but also to improve the life span and reliability of theelectronic memory media, in cases where the memory technology used haslimitations on write/erase cycles, as is the case with flash memory.Lifetime maximization may be achieved by avoiding unnecessary writeoperations as will be explained in greater detail below. For the purposeof further performance optimization, life span maximization, and costoptimization, the system may employ more than a single type of memorytechnology, including a mix of more than one Flash technology (e.g.,single level cell—SLC flash and multilevel cell—MLC flash), and a mix ofFlash and DRAM technologies. The data mapping optimizes performance andlife span by taking advantage of the different access speeds anddifferent write/erase cycle limitations of the various memorytechnologies.

The core method for mapping blocks of data internally within the systemis based on Content Addressing, and is implemented through a distributedContent Addressable Storage (CAS) algorithm.

This scheme maps blocks of data internally according to their content,resulting in mapping of identical block to the same unique internallocation. The distributed CAS algorithm allows for scaling of the CASdomain as overall system capacity grows, effectively utilizing andbalancing the available computational and storage elements in order toimprove overall system performance at any scale and with any number ofcomputational and storage elements.

The system supports advanced In-line block level deduplication, whichmay improve performance and save capacity.

Elements of the system's functionality are: Write (store) data block ata specified user address; Trim data block at a specified user address;Read data block from a specified user address; and In-line block leveldeduplication.

The following features may be provided: (1) A distributed CAS basedstorage optimized for electronic random-access storage media; Theoptimization includes utilizing storage algorithms, mainly thecontent-based uniformly-distributed mapping of data, that inherentlyspread data in a random way across all storage devices. Suchrandomization of storage locations within the system while maintaining avery high level of performance is preferably achievable with storagemedia with a high random access speed; (2) A distributed storagearchitecture with separate control and data planes; Data mapping thatmaximizes write-endurance of storage media; System scalability; (3)System resiliency to fault and/or failure of any of its components; (4)Use of multi-technology media to maximize write-endurance of storagemedia; and (5) In-line deduplication in ultrahigh performance storageusing electronic random-access storage media.

The examples described herein implement block storage in a distributedand scalable architecture, efficiently aggregating performance from alarge number of ultra-fast storage media elements (SSDs or other),preferably with no performance bottlenecks, while providing in-line,highly granular block-level deduplication with no or little performancedegradation.

One challenge is to avoid performance bottlenecks and allow performancescalability that is independent of user data access patterns.

The examples described herein may overcome the scalability challenge byproviding data flow (Write, Read) that is distributed among an arbitraryand scalable number of physical and logical nodes. The distribution isimplemented by (a) separating the control and data paths (the “C” and“D” modules), (b) maintaining optimal load balancing between all Datamodules, based on the content of the blocks (through the CAS/hashingmechanisms), hence ensuring always balanced load sharing regardless ofuser access patterns, (c) maintaining optimal load balancing between allControl modules, based on the user address of the blocks at finegranularity, hence ensuring always balanced load sharing regardless ofuser access patterns, and (d) performing all internal data pathoperations using small granularity block size, hence detaching theincoming user access pattern from the internal access pattern, since theuser pattern is generally larger than the block size.

A second challenge is to support inline, highly granular block leveldeduplication without degrading storage (read/write speed) performance.The result should be scalable in both capacity—which is deduplicatedover the full capacity space—and performance.

The solution involves distributing computation-intensive tasks, such ascalculating cryptographic hash values, among an arbitrary number ofnodes. In addition, CAS metadata and its access may be distributed amongan arbitrary number of nodes. Furthermore, data flow algorithms maypartition read/write operations in an optimally-balanced way, over anarbitrary and scalable number of Nodes, while guaranteeing consistencyand inline deduplication effect over the complete storage space.

In detaching the data from the incoming pattern, the R-Module breaks upany incoming block which is larger than the granularity size acrosssub-LUNs, sending the relevant parts to the appropriate C-Modules. EachC-module is predefined to handle a range or set of Sub-LUN logicaladdresses. The C-Module breaks up the block it receives for distributionto D-Modules, at a pre-determined granularity, which is the granularityfor which a Hash is now calculated. Hence the end result is that arequest to write a certain block (for example of size 64 KB) ends upbeing broken up into for example 16 internal writes, each writecomprising a 4 KB block.

The specific numbers for granularity can be set based on various designtradeoffs, and the specific number used herein of 4 KB is merely anexample. The broken down blocks are then distributed to the D modules inaccordance with the corresponding hash values.

A further challenge is to address flash-based SSD write/erase cyclelimitations, in which the devices have a lifetime dependent on thenumber of write/erase cycles.

The solution may involve Inline deduplication to avoid writing in allcases of duplicate data blocks. Secondly, content (hash) based mappingto different data modules and SSDs results in optimal wear-leveling,ensuring equal spread of write operations to all data modules and SSDsindependently of the user data/address access patterns.

In the following a system is considered from a functional point of view.As described above with respect to FIG. 1, the system 10 is architectedaround four main functional Modules designated R (for Router), C (forControl), D (for Data), and H (for Hash). Being modular and scalable,any specific system configuration includes at least one of R, C, D, andH, but may include a multiplicity of any or all of these Modules.

Reference is now made to FIG. 2, which is a functional block diagram ofthe system in which an H module 200 is connected to an R module 202. TheR module is connected to both Control 204 and data 206 modules. The datamodule is connected to any number of memory devices SSD 208.

A function of the R Module 202 is to terminate SAN Read/Write commandsand mute them to appropriate C and D Modules for execution by theseModules. By doing so, the R Module can distribute workload over multipleC and D Modules, and at the same time create complete separation of theControl and Data planes, that is to say provide separate control anddata paths.

A function of the C Module 204 is to control the execution of aRead/Write command, as well as other storage functions implemented bythe system. It may maintain and manage key metadata elements.

A function of the D Module 206 is to perform the actual Read/Writeoperation by accessing the storage devices 208 (designated SSDs)attached to it. The D module 206 may maintain metadata related with thephysical location of data blocks.

A function of the H Module is to calculate the Hash function value for agiven block of data.

Reference is now made to FIG. 3, which illustrates nodes. The R, C, D,and H Modules may be implemented in software, and executed on a physicalNode. A system includes at least one physical Node, and may includemultiple Nodes. There are four possible Node configurations: ComputeNode 300, which includes control and hash modules, Compute+SAN Node 302which includes a router as well as control and hash modules,Compute+Store Node 306, which includes a data module in addition tocompute and hash modules, and a Compute+SAN+Store Node 306, whichincludes all four modules. A system includes a storage area networkingor SAN function within at least one Node, and a Store function within atleast one Node. The SAN function and the store function can be supportedby the same physical Node or any combination of multiple Nodes.

In FIG. 3 each node type shows the functional Modules that execute, inat least one copy, within the Node, and functional Modules that mayoptionally execute within this Node. Optional Modules are shown indashed line.

All Nodes include a switch interface 308, to allow interconnecting witha switch in a multi-Node system configuration. A Node that contains aSAN function includes at least one SAN Interface module 310 and at leastone R Module. A Node that contains a Store function includes at leastone SSD Driver Module 312 and at least one D Module. Hence, Compute+SANand Compute+SAN+STORE Nodes contain a SAN Interface, to interface withthe external SAN. The interface may typically use a SCSI-based protocolrunning on any of a number of interfaces including Fiber Channel,Ethernet, and others, through which Read/Write and other storagefunction commands are being sent to the system. Compute+Store andCompute+SAN+Store Nodes contain an SSD driver 312 to interface with SSDs208 attached to that specific Node, where data is stored and accessed.

Reference is now made to FIG. 4, which shows a high level system blockdiagram. A system implementation includes one or more Nodes 400, 402. Inall cases where a system contains more than two Nodes, all physicalNodes are interconnected by a switch 404 which may be based on any of anumber of networking technologies including Ethernet, InfiniBand and soforth. In the specific case of a 2-Node system, the two Nodes can beinterconnected directly without a need for a switch.

The interconnections between each Node and the Switch may includeredundancy, so as to achieve high system availability with no singlepoint of failure. In such a case, each Node may contain two or moreSwitch Interface modules 406, and the Switch may contain two or moreports per physical Node.

As an example FIG. 5 illustrates a single Node system configuration, inwhich R, C and D modules, 500, 502 and 504 respectively are together ina compute+SAN+Store node 506. A switch interface 508 links to a switch.A SAN interface 510 provides an interface for storage area networking.An SSD driver 512 interfaces with the storage devices.

A four node system configuration is shown in FIG. 1 above. Theconfiguration includes two compute and store nodes and two compute+SANnodes.

A system that is built from multiple physical Nodes can inherentlysupport a high availability construction, where there is no single pointof failure. This means that any Node or sub-Node failure can becompensated for by redundant Nodes, having a complete copy of thesystem's meta-data, and a complete redundant copy of stored data (orparity information allowing recovery of stored data). The distributedand flexible architecture allows for seamless support of failureconditions by simply directing actions to alternate Nodes.

The R module is responsible for routing SCSI I/O requests to the Cmodules, guarantee execution and return the result; and balancing thework load between the C modules for the requests it is routing.

An A→C table indicates which C module is responsible for each logicalX-page address (LXA). Each C module is responsible for a list of SubLUNs (SLs).

The R module receives requests for I/Os from the SAN INTERFACE routesthem to the designated C modules and returns the result to the SANINTERFACE.

If an I/O operation spans across multiple SLs, and perhaps multiple Cmodules, then the R module has the responsibility of breaking the bigI/O operation into multiple smaller independent operations according tothe sub LUN unit size (SLUS). Since the atomic unit size (AUS) is neverlarger than the SLUS, as explained in greater detail below, each suchI/O is treated as an independent operation throughout the system. Theresults may then be aggregated before returning to the SAN INTERFACE.

The R module is responsible for maintaining an up-to-date A→C tablecoordinated with the MBE. The A→C table is expected to balance the rangeof all possible LXAs between the available C modules.

For write operations, the R module instructs the calculation of the hashdigest for each X-Page by requesting such calculation from a Hashcalculation module.

The C module is responsible for: receiving an I/O request from an Rmodule on a certain SL, guaranteeing its atomic execution and returningthe result; communicating with D modules to execute the I/O requests;monitoring the disk content of its SLs' logical space by associatingeach LXA with its hash digest; and balancing the work load between the Dmodules for the SLs it is maintaining.

An H→D table maps each range of hash digests to the corresponding Dmodule responsible for this range.

An A→H table maps each LXA that belongs to the SLs C is responsible for,to the hash digest representing the X-Page Data that currently residesin this address.

The C module receives I/O requests from R modules, distributes the workto the D modules, aggregates the results and guarantees an atomicoperation. The result is returned to the R module.

The C module maintains an up-to-date H→D table coordinated with the MBE.The table is expected to balance the range of all possible hash digestsbetween the available D modules.

The C module maintains an A→H table in a persistent way. The C modulemay initiate 110 requests to D modules in order to save table pages todisk, and read them from disk. To avoid frequent disk operations, aJournal of the latest table operations may be maintained.

Data is balanced between the C modules based on the logical address, atthe granularity of sub-LUNs.

The D module is responsible for: maintaining a set of LUNs which areattached locally and performing all I/O operations on these LUN;managing the physical layout of the attached LUNs; managing the mappingbetween X-Page Data hash digests and their physical location in apersistent way; managing deduplication of X-Page Data in a persistentway; and receiving disk I/O requests from C modules, perform them andreturning a result.

The D module is also responsible for, for each write operation, backingup the X-Page Data in the designated D backup module and performingread-modify operations for writes that are smaller than X-Page size(This process also involves computing a hash digest for these X-Pages).

The D module is further responsible for maintaining an up-to-date H→(D,D_(backup)) table coordinated with the MBE. The H→(D, D_(backup)) tableis expected to balance the range of all possible hash digests betweenthe available D modules.

The D module does not communicate directly with R modules. The onlyinteraction with R modules involves RDMA read/write operations of X-PageData.

Balancing between the D modules is based on hashing of the content.

The D module makes use of a hash digest metadata table. The hash digestmetadata table maps each in use hash digest, that represents actualX-Page Data, to its meta data information including its physical page onthe storage media (SSD), its memory copy (if exists), a mapping to anybackup memory copy and a reference count for the purpose ofdeduplication.

A further structure used is the H→(D, D_(backup)) table. The H→(D,D_(backup)) table maps each range of hash digests to the corresponding Dmodule responsible for the range as well as the D_(backup) moduleresponsible for the range.

The D modules allocate a physical page for each X-Page. The D modulesalso manage the memory for the physical storage. They allocate memorypages for read/write operations and perform background destaging frommemory to storage media when necessary, for example, when running low onmemory.

The D modules manage a separate nonvolatile memory pool (NVRAM or UPSprotected) for X-Page Data backup purposes. The backup holds X-Pagesthat are held in memory of the D primary and have not yet been destaged.When re-balancing between D modules occur (due to a D module failure forexample), the D module may communicate with other D modules in order tocreate new backup copies or move a primary ownership as required.

The D modules allow deduplication per X-Page Data by maintaining apersistent reference count that guarantees only one copy per X-PageData. The D modules manage the hash digest metadata table in apersistent way. The table is coordinated with the physical layout forphysical pages allocation, with the memory pointer, memory backuppointer and deduplication reference count.

The D modules receive I/O requests from C modules, perform the requestswhile supporting deduplication and return the result. The D modules mayperform RDMA read/write operations on memory that resides in othermodules, such as R modules as mentioned above, as part of the I/Ooperation.

When a write operation smaller than the size of an X-Page is received,the D module may read the entire X-Page to memory and perform partialX-Page modification on that memory. In this case race conditions mayoccur, for example when two small writes to the same X-Page occur inparallel, and the D module may be required to compute the hash digest ofthe resulting X-Page. This is discussed in greater detail below.

The H-Module calculates the Hash function of a given block of data,effectively mapping an input value to a unique output value. The Hashfunction may be based on standards based hash functions such as SHA-1and MD5, or based on a proprietary function. The hash function isselected to generate a uniformly distributed output over the range ofpotential input values.

The H modules usually share nodes with an R module but more generally,the H modules can reside in certain nodes, in all nodes, together with Rmodules, or together with C or D modules.

The following discussion provides high level I/O flows for read, writeand trim.

Throughout these flows, unless noted otherwise, control commands arepassed between modules using standard RPC messaging, while data “pull”operations may use RDMA read. Data push (as well as Journal) operationsmay use RDMA write.

The read flow of one X-Page may consist of one R module which receivesthe read request from the application, one C module in charge of theaddress requested and one D module which holds the X-Page to be read.Larger, or unaligned, requests may span several X-Pages and thus mayinvolve several D modules. These requests may also span several SLs, inwhich case they may involve several C modules as well.

Reference is now made to FIG. 6 which illustrates the flow for a readoperation for one aligned X-page. When the R module receives a readrequest from an application the R module allocates a request ID for theoperation; translates the LBA to LXA; allocates a buffer for the data tobe read; consults the A→C component to determine which C module is incharge of this LXA; and sends the designated C module a read requestwhich includes parameters that include a request ID; an LXA; and apointer to the allocated buffer.

The C module, when receiving the request, consults the A→H component,from which it obtains a hash digest representing the X-Page to be read;consults the H→D component to determine which D module holds the X-Pagein question; and sends this D module a read request which includesparameters that include a request ID (as received from the R module),the hash digest, a pointer to the buffer to read to, as received fromthe R module; and an identifier of the R module.

The D module, when receiving the request, reads the data of therequested X-Page from SSD and performs an RDMA write to the requesting Rmodule, specifically to the pointer passed to it by the C module.

Finally the D module returns success or error to the requesting Cmodule.

The C module in turn propagates success or error back to the requestingR module, which may then propagate it further to answer the application.

Reference is now made to FIG. 7, which illustrates the flow in the casethat a read request arrives for a range of addresses spanning more thanone X-Page but only one SL. In such a case the R module sends thedesignated C module a read command with the parameters that include arequest ID, first LXA, size of the requested read in X-Pages-n, and npointers to the allocated X-Page buffers.

The rest of the R module's treatment is identical to the aligned oneX-Page scenario previously described herein.

The C module, when receiving the request divides the logical addressspace to LXAs. For each LXA the C module consults the A→H component todetermine the corresponding hash digest; consults the H→D table todetermine which D module is responsible for the current LXA; sends eachD module a read command containing all the hashes that the respective Dmodule is responsible for. The parameters of the read command include arequest ID (as received from the R module); a list of respectivehash-pointer pairs; and the identifier of the R module.

Each D module, when receiving the request, acts per hash-pointer pair inthe same manner as described above for one X-Page. Aggregated success orerror is then sent to the requesting C module.

The C module aggregates all the results given to it by the D modules andreturn success or error back to the requesting R module, which may thenanswer the application.

In the case that a read request spans multiple SLs, the R module splitsthe request and sends several C modules read requests. Each C module mayreceive one request per SL. The flow may continue as in the simpler caseabove, except that now the R module aggregates the responses before itanswers the application.

Read requests smaller than 4 KB, as well as requests not aligned to 4KB, may be dealt with at the R module level. For each such parcel ofdata, the R module may request to read the encompassing X-Page. Uponsuccessful completion of the read command, the R module may crop thenon-relevant sections and return only the requested data to theapplication.

The write flow of one X-Page may consist of one R module which receivesthe write request from the application, one C module in charge of theaddress requested and three D modules: D_(target) which is in charge ofthe X-Page Data to be written (according to its appropriate hashdigest), D_(old) which was in charge of the X-Page Data this addresscontained previously (“old” hash digest), and D_(backup) in charge ofstoring a backup copy of the X-Page Data to be written.

Reference is now made to FIG. 8, which is a simplified flow diagramillustrating the write procedure for a single aligned X page accordingto the examples described herein.

When an R module receives a write request from the application, the Rmodule allocates a request ID for this operation; translates the LBA toan LXA; computes a hash digest on the data to be written; consults itsA→C component to determine which C module is in charge of the currentLXA; and sends the designated C module a write command with parametersthat include a request ID; an LXA; a hash digest; and a pointer to thebuffer containing the data to be written.

The C module, when receiving the request consults its H→D component tounderstand which D module is in charge of the X-Page to be written(D_(target)); and sends D_(target) a write request with parameters thatinclude the request ID (as received from the R module); the hash digest(as received from the R module); the pointer to the data to write (asreceived from the R module); and the identifier of the R module.

The D module receiving the write command, D_(target), may first check ifit already holds an X-Page corresponding to this hash. There are twooptions here:

First, D_(target) does not have the X-Page. In this case D_(target)fetches the data from the R module using RDMA read and stores it in itsmemory; consults the H→D component to determine which D module is incharge of storing a backup copy of this X-Page (D_(backup)); performs anRDMA write of the X-Page Data to the D_(backup) backup memory space; andreturns success (or failure) to the C module.

Second, D_(target) has the X-Page. In this case D_(target) increases thereference count, returns success (or failure) to the C module.

The C module waits for a response from D_(target). If a success isreturned, the C module updates the A→H table to indicate that the LXA inquestion should point to the new hash and returns a response to therequesting R module.

If this is not a new entry in the A→H table, the C module asynchronouslysends a decrease reference count command to D_(old) (the D moduleresponsible for the hash digest of the previous X-Page Data). Thesecommands may be aggregated at the C module and sent to the D modules inbatches.

The R module may answer the application once it receives a response fromthe C module.

Reference is now made to FIG. 9, which is a flow diagram illustratingthe process for writes to multiple full X-Pages.

In the case that the write request spans a range of addresses whichinclude more than one X-Page but only one SL, the R module sends thedesignated C module a write command with parameters that include arequest ID; a first LXA; a size of the requested write in LXAs-n; andH_(BIG) which is a unique identifier of the entire chunk of data to bewritten. Her may be a computed hash digest and thus equal for twoidentical chunks of data.

Additional parameters sent with the write command are n pointers thatpoint to the buffers which hold the data to be written.

The rest of the R module treatment is the same as for the aligned oneX-Page scenario.

The C module, when receiving the request, consults its H→D component tounderstand which D module is in charge of H_(BIG)(D_(target)) andgenerates a hash digest per pointer by replacing one byte of H_(BIG)with the offset of that pointer. It is noted that this byte must notcollide with the bytes used by the H→D table distribution.

It may send D a write request with the parameters that include therequest ID (as received from the R module); a list of respectivehash-pointer pairs; and the Identifier of the R module.

The D module, when receiving the request, acts per hash-pointer pair inthe same manner as described above for one X-Page. Aggregated success orerror is then sent to the requesting C module.

The C module waits for a response from D_(target). If the responseindicates success, the C module updates its A→H table to indicate thatthe LXAs in question should point to the new hashes. Updating of entriesin the A→H table may be done as an atomic operation, to ensure the writerequest is atomic. Note that all requests aligned to 4 KB (or anotherpredefined block size) that fall within a SL may be atomic. The C modulereturns a response to the requesting R module. The C module adds thelist of old hashes to the “decrease reference” batch if needed.

The R module answers the application once it receives a response fromthe C module.

In the case in which a write request spans multiple SLs, the R modulesplits the request and sends smaller write requests to several Cmodules. Each C module receives one request per SL (with a uniquerequest ID). The flow continues as in the simpler case above, exceptthat now the R module aggregates the responses before it answers theapplication.

Referring to FIG. 10, a replication system 1000 may be implemented usingthe system 10. The replications system 1000 includes a source storage(e.g., a storage array) 1002 (on a source side), a target storage (e.g.,a storage array) 1004 (on a target side), a replicator 1008 and a host1010. The host 1010 may include an application (not shown) that writesto the source storage 1002. The replicator 1008 ensures that all thewrites made to the source storage 1002 are also eventually made to thetarget storage 1004.

The source storage 1002 includes a volume 1032 a and the target storageincludes a volume 1032 b. In one example, the volume 1032 a isasynchronously replicated to the volume 1032 b.

Referring to FIG. 11, an example of a process to suspend datareplication is a process 1100. Process 1100 is not strictly a softsuspend or strictly a hard suspend. A soft suspend waits for currentdata transfer cycle to complete and pauses scheduling of future datatransfer. A hard suspend aborts ongoing data transfer, and restarts datatransfer from the beginning upon resume. A soft suspend preserves datatransfer work already done, however it takes an undetermined and anunbounded amount of time to reach the suspended state. A hard suspendhas the benefit of reaching suspended state immediately, but at the costof wasting all the data already transferred, which is especially costlywhen a suspend request comes near the end of a replication cycle. Areplication cycle is a complete delta data transfer from source todestination. Upon successful completion of a replication cycle, thereplication destination is synchronized with the data content of thesource at the beginning of the cycle. A replication cycle may includeone or more of the following actions: start a new cycle if the system isready, previous cycle complete, RPO/lag criteria met, resource availablefor replication, and so forth; generate a snapshot against the source,S_(src), which is the base of current cycle; generate a snapshot againstthe target, S_(tgt), which will be the container of the cycle datatransfer; scan and compute the difference between current cycle snap andprevious cycle snap, and transfer data different to target, complete thereplication cycle after the entire source snap data been scanned, anddata transfer completed (e.g., at this point, the data content ofS_(tgt) is the same as S_(src)); and preparation for the nextreplication cycle.

On the other hand, process 1100 allows a system to dynamically decidethe best suspend action based on, for example, customer priorities, acurrent condition of the system, ongoing replication progress andcombinations thereof.

Process 1100 monitors replication (1102). For example, the process 1100monitors progress of a replication data transfer by collecting andaggregating replication iteration I/O statistics from worker threads innodes. The I/O statistics collected during a replication cycle includebut are not limited to, how much data has been scanned and transferred,where is the current scan iterator in the address space, how much timeelapsed since start of the cycle, and so forth. A worker thread is athread which is assigned a subset of scanning and transfer work of thereplication cycle. In some examples, a replication cycle may launchmultiple worker threads, each of which scans a subset of data.

Process 1100 predicts performance (1108). For example, process 1100predicts an estimated time to complete a current replication cycle basedon progress, short term/long term history of cycle progressing rate, anindividual worker thread/node progress rate or any combination thereof.

Process 1100 receives a suspend notifications (1112). For example, auser sends a suspend command to the system 1100. In other examples,certain conditions such as other commands being executed or eventtriggers require that replication be suspended. For example, suspend maybe triggered by a resource contention, or link deterioration.

Process 1100 determines whether to suspend the replication data transfer(1120). For example, process 1100 uses the predicted performance todetermine whether to suspend now or wait based in the predictedperformance determined in processing block 1112. For example, process1100 dynamically determines a suspend deadline based on cycle completionprediction, user case scenarios, the condition of the system or anycombination thereof. In one particular example, if the replicationsystem 1000 system enters a degraded state and would like to suspendnon-essential I/O activity such as replication, suspend deadline will beimmediate; or if the replication source site encounters a disaster,suspend deadline will also be set to immediate so that the system couldquickly prepare for failover. On the other hand, in other examples, if asystem administrator needs to upgrade the system in a certainmaintenance window, the suspend deadline could be based on themaintenance window size and cycle completion prediction. A maintenancewindow is period of time designated in advance by the technical staff,during which preventive maintenance that could cause disruption ofservice may be performed. Process 1100 provides an optimal solution forboth responsiveness and efficiency with minimal administrative burden.

If the process 1100 determines to suspend now, process 1100 suspendsreplication (1142). For example, the system 1000 aborts current datatransfer, and enters a suspended state once abort is complete.

If the process 1100 determines that suspend does not need to occurimmediately, process 1100 determines if the replication rate should beincreased (1128). If the replication rate should be increased, process1100 increases the replication rate (1132). For example, if a certainuse case requires both to preserve data transfer and a fast suspend,process 1100 may determine to increase replication bandwidth andreplication I/O priority to speed up data transfer, so that theestimated time to complete falls within a desired deadline.

If the replication rate should not be increased (i.e., the predictedcycle complete time (e.g., even after a replication rate adjustment) iswell within suspend deadline), process 1100 waits for the end of thedata transfer (1136). For example, if estimated time to complete a datatransfer cycle is well within a suspend deadline, the system 1000 marksreplication for suspension and waits for current cycle completion, andthen switches into suspended state (e.g., processing block 1142).

Referring to FIG. 12, in one example, a computer 1200 includes aprocessor 1202, a volatile memory 1204, a non-volatile memory 1206(e.g., hard disk) and the user interface (UI) 1208 (e.g., a graphicaluser interface, a mouse, a keyboard, a display, touch screen and soforth). The non-volatile memory 1206 stores computer instructions 1212,an operating system 1216 and data 1218. In one example, the computerinstructions 1212 are executed by the processor 1202 out of volatilememory 1204 to perform all or part of the processes described herein(e.g., process 1100).

The processes described herein (e.g., process 1100) are not limited touse with the hardware and software of FIG. 12; they may findapplicability in any computing or processing environment and with anytype of machine or set of machines that is capable of running a computerprogram. The processes described herein may be implemented in hardware,software, or a combination of the two. The processes described hereinmay be implemented in computer programs executed on programmablecomputers/machines that each includes a processor, a non-transitorymachine-readable medium or other article of manufacture that is readableby the processor (including volatile and non-volatile memory and/orstorage elements), at least one input device, and one or more outputdevices. Program code may be applied to data entered using an inputdevice to perform any of the processes described herein and to generateoutput information.

The system may be implemented, at least in part, via a computer programproduct, (e.g., in a non-transitory machine-readable storage medium suchas, for example, a non-transitory computer-readable medium), forexecution by, or to control the operation of, data processing apparatus(e.g., a programmable processor, a computer, or multiple computers)).Each such program may be implemented in a high level procedural orobject-oriented programming language to communicate with a computersystem. However, the programs may be implemented in assembly or machinelanguage. The language may be a compiled or an interpreted language andit may be deployed in any form, including as a stand-alone program or asa module, component, subroutine, or other unit suitable for use in acomputing environment. A computer program may be deployed to be executedon one computer or on multiple computers at one site or distributedacross multiple sites and interconnected by a communication network. Acomputer program may be stored on a non-transitory machine-readablemedium that is readable by a general or special purpose programmablecomputer for configuring and operating the computer when thenon-transitory machine-readable medium is read by the computer toperform the processes described herein. For example, the processesdescribed herein may also be implemented as a non-transitorymachine-readable storage medium, configured with a computer program,where upon execution, instructions in the computer program cause thecomputer to operate in accordance with the processes. A non-transitorymachine-readable medium may include but is not limited to a hard drive,compact disc, flash memory, non-volatile memory, volatile memory,magnetic diskette and so forth but does not include a transitory signalper se.

The processes described herein are not limited to the specific examplesdescribed. For example, the process 1100 is not limited to the specificprocessing order of FIG. 11. Rather, any of the processing blocks ofFIG. 11 may be re-ordered, combined or removed, performed in parallel orin serial, as necessary, to achieve the results set forth above.

The processing blocks (for example, in the process 1100) associated withimplementing the system may be performed by one or more programmableprocessors executing one or more computer programs to perform thefunctions of the system. All or part of the system may be implementedas, special purpose logic circuitry (e.g., an FPGA (field-programmablegate array) and/or an ASIC (application-specific integrated circuit)).All or part of the system may be implemented using electronic hardwarecircuitry that include electronic devices such as, for example, at leastone of a processor, a memory, a programmable logic device or a logicgate.

Elements of different embodiments described herein may be combined toform other embodiments not specifically set forth above. Variouselements, which are described in the context of a single embodiment, mayalso be provided separately or in any suitable subcombination. Otherembodiments not specifically described herein are also within the scopeof the following claims.

What is claimed is:
 1. A method comprising: monitoring a replicationprocess; predicting performance based on the monitoring; receiving asuspend notification; and determining whether to suspend the replicationprocess or wait to suspend the replication process based on theperformance predicted.
 2. The method of claim 1, wherein determiningwhether to suspend the replication process or wait to suspend thereplication process based on the performance predicted comprisesdetermining whether the data replication will complete before a suspenddeadline.
 3. The method of claim 2, further comprising increasing thereplication rate so that the data replication will complete before thesuspend deadline.
 4. The method of claim 1, wherein monitoring areplication process comprises monitoring progress of a replication datatransfer by collecting and aggregating replication iteration I/Ostatistics from worker threads in nodes.
 5. The method of claim 1,wherein predicting performance based on the monitoring comprisespredicting an estimated time to complete a current replication cyclebased on at least one of progress, short term/long term history of cycleprogressing rate, and individual worker thread/node progress rate. 6.The method of claim 1, wherein determining whether to suspend thereplication process or wait to suspend the replication process based onthe performance predicted comprises determining a suspend deadline basedon at least one of a cycle completion prediction, user case scenario, acondition of a replication system or a maintenance window size.
 7. Anapparatus, comprising: electronic hardware circuitry configured to:monitor a replication process; predict performance based on themonitoring; receive a suspend notification; and determine whether tosuspend the replication process or wait to suspend the replicationprocess based on the performance predicted.
 8. The apparatus of claim 7,wherein the circuitry comprises at least one of a processor, a memory, aprogrammable logic device or a logic gate.
 9. The apparatus of claim 7,wherein the circuitry configured to determine whether to suspend thereplication process or wait to suspend the replication process based onthe performance predicted comprises circuitry configured to determinewhether the data replication will complete before a suspend deadline.10. The apparatus of claim 9, further comprising circuitry configured toincrease the replication rate so that the data replication will completebefore the suspend deadline.
 11. The apparatus of claim 7, wherein thecircuitry configured to monitor a replication process comprisescircuitry configured to monitor progress of a replication data transferby collecting and aggregating replication iteration I/O statistics fromworker threads in nodes.
 12. The apparatus of claim 7, wherein circuitryconfigured to predict performance based on the monitoring comprisescircuitry configured to predict an estimated time to complete a currentreplication cycle based on at least one of progress, short term/longterm history of cycle progressing rate, and individual workerthread/node progress rate.
 13. The apparatus of claim 7, whereincircuitry configured to determine whether to suspend the replicationprocess or wait to suspend the replication process based on theperformance predicted comprises circuitry configured to determine asuspend deadline based on at least one of a cycle completion prediction,user case scenario, a condition of a replication system or a maintenancewindow size.
 14. An article comprising: a non-transitorycomputer-readable medium that stores computer-executable instructions,the instructions causing a machine to: monitor a replication process;predict performance based on the monitoring; receive a suspendnotification; and determine whether to suspend the replication processor wait to suspend the replication process based on the performancepredicted.
 15. The article of claim 14, wherein the instructions causingthe machine to determine whether to suspend the replication process orwait to suspend the replication process based on the performancepredicted comprises instructions causing the machine to determinewhether the data replication will complete before a suspend deadline.16. The article of claim 15, further comprising instructions causing themachine to increase the replication rate so that the data replicationwill complete before the suspend deadline.
 17. The article of claim 14,wherein the instructions causing the machine to monitor a replicationprocess comprises instructions causing the machine to monitor progressof a replication data transfer by collecting and aggregating replicationiteration I/O statistics from worker threads in nodes.
 18. The articleof claim 14, wherein instructions causing the machine to predictperformance based on the monitoring comprises instructions causing themachine to predict an estimated time to complete a current replicationcycle based on at least one of progress, short term/long term history ofcycle progressing rate, and individual worker thread/node progress rate.19. The article of claim 14, wherein instructions causing the machine todetermine whether to suspend the replication process or wait to suspendthe replication process based on the performance predicted comprisesinstructions causing the machine to determine a suspend deadline basedon at least one of a cycle completion prediction, user case scenario, acondition of a replication system or a maintenance window size.